When the tasks at hand demand both knowledge of theory and the creation of something novel, one woman is making her mark in the world of finance – Harshita. This complements her achievements and work in the financial sector which portrays Harshita as a woman who is, talented, collaborative, and invitingly fierce in business ideas and implementation. So, in relation to mechanical engineering, where she possesses a relevant academic background and mid-career growth, more precisely, her involvement in preparing future mechanical engineering professionals for data interpretation and decision-making is for her a transformational view of education in every regard. Therefore, for Harshita, all such activities are part of the learning process and he cannot be blamed for only focusing on site-based tasks which in his case include collections strategy at Barclays, consultancy in digital change and risk management at JP Morgan Chase, and operational enhancement at Amazon.
In this exclusive Q&A, we present what seemed to be clueless career achievements in relation to data
regarding Harshita’s biography and the decision-making data she was able to transform for critical directions and the directions she took.
In your opinion, how did your education and experience in mechanical engineering facilitate your move into data analytics and management?
A: By training in the field of mechanical engineering, a certain mental framework within the bounds of design concepts such as optimization, evaluation of various alternatives, and systematic approach to problems was acquired. Thus, it comes as no surprise that engineering is ‘understood as design’, more or less, about comprehending complicated systems and refining them. The applied mechanical engineering skills with statistics, numerical computations, finite element structural analysis, optimization of processes and data analysis, and essential skills for a data scientist analytics are mentioned in its essence. Moreover, those practices enabled me to adopt a systematic and detail-oriented approach to data which is very important for strategy, data management, and optimization.
- What caused you to abandon mechanical engineering in this study, information technology, and data analysis in particular?
- A: The reason was simple, it was a more humane way of viewing how data could be more optimized in business or product or service engineering. I saw the benefits of a more technical education such as mechanical engineering, but then came to appreciate that primarily, I want to apply the data to make business decisions. There were also emerging data sciences and other forms of advanced analytics that allowed me to find further ways to apply my analytical thinking in a wider view of business strategy. This transition enabled me to start viewing data as more than just numbers – it became possible to use it to formulate solutions, improve processes, and deliver significant cost reductions which was indeed very rewarding.
- The message relaunch project you worked on, can you comment why it is of significance?
A: Barclays placed a great emphasis on the relaunch of text messaging as a collections tool, the project for such a development has been on pause for 13 years. There became available a large amount of this data and it became possible to create a very rich electronic form and content for the customers which allows us to greatly enhance customer service and decrease risks associated with delinquency. The initiative was complicated by phased rollout, performance monitoring, and evaluation of effectiveness to allow for an accurate assessment of the legal compliance and risks involved in venturing into new and potentially high-risk
collection strategies and practices. This development achieved impairment savings of $40 million which demonstrates the extent to which traditional strategies can be effective when synergised with modern
strategies. It also showed how crucial it is to be innovative to maintain or enhance organizational financial
performance by adopting new means of interacting with the organization’s customers. - What impact did the predictive modeling and statistical assessments you conducted have on the strategic
suggestions you provided?
A: I would say discussions around predictive modeling and statistical examinations typically lead to the
recommendations being provided. With such techniques, I managed to evaluate historical data, detect trends, and forecast the developments of future events quite comfortably. This initiated the formulation of specific strategies for solving specific business issues. For instance, the role played by the predictive models in the improvement of the credit and operational strategies improved the management of risks and cost efficiency. - Describe an example of a data-driven project, including one that was very challenging but how you coped
with the same challenges.
A: Implementing machine learning models for customer segmentation and predicting probabilities of
delinquency at JP Morgan Chase & Co. was noteworthy among the projects. This was a challenge in the
development stage of the model as it was required to incorporate the above, which presented an unusual
circumstance – the COVID-19 period – into its future delinquency forecasts. The assignment was to
incorporate methods that were able to fully complement the different types of data and ensure that the
information about COVID-19 did not distort the overall analysis. This turned out to be possible through careful data cleansing, effective model evaluation and testing, and constant modification. The active interaction of cross-functional teams and the use of advanced algorithms made it possible to implement measures that increased operational and cost efficiency.
- How important is data visualization to your decision-making, and UTC’s decision-making? And for the
success story, how do Geometry and Power BI relate to that?
A: Data Visualization, tools, heart, and all other terms that refer to visualization. Geometry and Power BI, for example, enable us to create and develop great visual representations of data that facilitate better
communication about key numbers and trends. Such constructs tell us how, when, and in what form the
information is likely to be needed enabling rapid interventions to be made. Effective data visualization also contributes to the effective perception of the trends and patterns that are often difficult to detect in tables of ordinary numerical data. - What is your perception of the experience that you gained especially at Amazon in the improvement of the scalability metrics as these add value to you and your skills as a person?
A: At Amazon more specifically enhancing scalability metrics was an important accomplishment. Improvement of salability metrics from 30.64% to 90.27% led to turning unnecessary loss units into sellable units thus bearing great cost. In this experience, I felt how vital resources are in fusing management with operational efficiency. It also honed my skills in stats analysis, big data, performance adjustments, and data visualization which were very essential in the next jobs. - What procedures, steps, or measures do you undertake to improve the data extraction and validation
procedures in your projects and processes?
A: Data extraction, and more specifically data cleaning, is one of the processes that requires several measures to be effective. For instance, these measures include data cleaning protocols, automated checking of data, and data validation from many sources. I emphasize, in addition, the use of more complete statistical methods for data verification. Usually, such quality control and comparative approaches with other information sources are offered in the process of these operations to ensure that the collected data is of high standards and therefore the conclusions arrived at are relevant and accurate. - What measures do you adopt to integrate the developed machine learning models into the business
processes for which they are designed?
A: It is pertinent to note that the challenge of deploying machine learning models within an already existing business model is not an easy one. It calls for analysis of the business processes, diagnosis of the possible functional gaps, and adoption of such processes. Testing and validation are also heads of development and deployment that I make frequent use of. Other measures that can be employed are working with the relevant staff and other stakeholders to incorporate practical models into the company’s operations as well as making model performance amendments if it is necessary. - To what extent do your academic training and the certifications you have earned account for the success
that you have achieved in your career?
A: My education and the certifications I bear have contributed to my payoff professionally to a great extent. It should be mentioned that my graduation from the National Institute of Technology Warangal which could be referenced as having an acceptance rate of less than one percent helped me acquire some elementary engineering technical and analytical knowledge. Later, I managed to maintain higher standards of performance which saw me being awarded the Dean’s Excellence Scholarship when undertaking my masters in Information Technology Management and being recognized as a Scholar of High Distinction. In addition, professional certificates like Tableau Desktop Specialist, for example, have also increased my technical skills in the use of such advanced tools Example. Such an education followed by relevant competencies has also equipped me with the skills and knowledge necessary for career advancement, for dealing with difficult responsibilities, and for the formulation and implementation of successful and creative strategies and plans. With the combination of an engineering background coupled with leadership in data analytics and risk strategy as demonstrated by Harshita Cherukuri, it is readily apparent how interdisciplinary knowledge and motivation for growth combine effectively. Developing workable solutions comes naturally to her as she is in charge of eliciting strategic objectives, which span the technical and business domains. Owing to Harshita’s energetic and original ideas, the organization would interface with data differently and become data-centric. Because she continues to push the frontiers of
(Also Read: Airtel Vs Jio Vs Starlink: Will Airtel And Jio Slash Tarrif Rates After The Entry Of Elon Musk’s Starlink In India?)
